search for: daic

Dear R users, Could somebody please help me to find a way of comparing nonlinear, non-nested models in R, where the number of parameters is not necessarily different? Here is a sample (growth rates, y, as a function of internal substrate concentration, x): x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48) y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98,

...derive a correct G2 value when rcs() functions are used in an lrm() model? e.g., lrm.iRVI <- lrm(arson ~ rcs(iRVI,knots), penalty=list(simple=10,nonlinear=100,nonlinear.interaction=4)) but this does not work. Is the AIC value a good approx for the L.R. model? (see function below) dAIC <- function(fit){ logl <- oos.loglik(fit) # derive -2*logL of the model k <- 2 edf <- fit$stats[4] # 'edf' is the equivalent degrees of freedom (i.e., the number of parameters for usul parametric models) of 'fit'. lrmAIC...

I am trying to create a frequency distribution and I am a bit confused. Here are the commands I have entered: > data <- read.csv(file="40609_sortedfinal.csv",head=TRUE,sep=",") > NumberOfActionsByStatus = data$STATUS > NumberOfActionsByUser = data$ETS_LOGIN > NumberOfBidOffer = data$BID_OFFER > NumberOfActionsByUser.freq = table(NumberOfActionsByUser) >